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Illuminating Economic Growth

Author

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  • Yingyao Hu
  • Jiaxiong Yao

Abstract

This paper seeks to illuminate the uncertainty in official GDP per capita measures using auxiliary data. Using satellite-recorded nighttime lights as an additional measurement of true GDP per capita, we provide a statistical framework, in which the error in official GDP per capita may depend on the country’s statistical capacity and the relationship between nighttime lights and true GDP per capita can be nonlinear and vary with geographic location. This paper uses recently developed results for measurement error models to identify and estimate the nonlinear relationship between nighttime lights and true GDP per capita and the nonparametric distribution of errors in official GDP per capita data. We then construct more precise and robust measures of GDP per capita using nighttime lights, official national accounts data, statistical capacity, and geographic locations. We find that GDP per capita measures are less precise for middle and low income countries and nighttime lights can play a bigger role in improving such measures.

Suggested Citation

  • Yingyao Hu & Jiaxiong Yao, 2019. "Illuminating Economic Growth," IMF Working Papers 2019/077, International Monetary Fund.
  • Handle: RePEc:imf:imfwpa:2019/077
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    References listed on IDEAS

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    1. Shuaizhang Feng & Yingyao Hu, 2013. "Misclassification Errors and the Underestimation of the US Unemployment Rate," American Economic Review, American Economic Association, vol. 103(2), pages 1054-1070, April.
    2. Rafael La Porta & Andrei Shleifer, 2014. "Informality and Development," Journal of Economic Perspectives, American Economic Association, vol. 28(3), pages 109-126, Summer.
    3. Joachim Freyberger, 2017. "On Completeness and Consistency in Nonparametric Instrumental Variable Models," Econometrica, Econometric Society, vol. 85, pages 1629-1644, September.
    4. S. M. Schennach & Yingyao Hu, 2013. "Nonparametric Identification and Semiparametric Estimation of Classical Measurement Error Models Without Side Information," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(501), pages 177-186, March.
    5. J Vernon Henderson & Tim Squires & Adam Storeygard & David Weil, 2018. "The Global Distribution of Economic Activity: Nature, History, and the Role of Trade1," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 357-406.
    6. Adam Storeygard, 2016. "Farther on down the Road: Transport Costs, Trade and Urban Growth in Sub-Saharan Africa," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 83(3), pages 1263-1295.
    7. Leandro Medina & Mr. Friedrich Schneider, 2018. "Shadow Economies Around the World: What Did We Learn Over the Last 20 Years?," IMF Working Papers 2018/017, International Monetary Fund.
    8. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, January.
    9. Raymond Carroll & Xiaohong Chen & Yingyao Hu, 2010. "Identification and estimation of nonlinear models using two samples with nonclassical measurement errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(4), pages 379-399.
    10. Dave Donaldson & Adam Storeygard, 2016. "The View from Above: Applications of Satellite Data in Economics," Journal of Economic Perspectives, American Economic Association, vol. 30(4), pages 171-198, Fall.
    11. Hu, Yingyao, 2017. "The econometrics of unobservables: Applications of measurement error models in empirical industrial organization and labor economics," Journal of Econometrics, Elsevier, vol. 200(2), pages 154-168.
    12. Maxim Pinkovskiy & Xavier Sala-i-Martin, 2016. "Lights, Camera … Income! Illuminating the National Accounts-Household Surveys Debate," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 579-631.
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    Cited by:

    1. Eduardo Souza-Rodrigues & Adrian L. Torchiana & Ted Rosenbaum & Paul T. Scott, 2020. "Improving Estimates of Transitions from Satellite Data: A Hidden Markov Model Approach," Working Papers tecipa-672, University of Toronto, Department of Economics.
    2. Areendam Chanda & C. Justin Cook, 2019. "Who Gained from India's Demonetization? Insights from Satellites and Surveys," Departmental Working Papers 2019-06, Department of Economics, Louisiana State University.
    3. Bluhm, Richard & Krause, Melanie, 2022. "Top lights: Bright cities and their contribution to economic development," Journal of Development Economics, Elsevier, vol. 157(C).
    4. Rangel González Erick & Llamosas-Rosas Irving, 2021. "Observing the Evolution of the Informal Sector from Space: A Municipal Approach 2013-2020," Working Papers 2021-18, Banco de México.
    5. Jaqueson K Galimberti & Stefan Pichler & Regina Pleninger, 2023. "Measuring Inequality Using Geospatial Data," The World Bank Economic Review, World Bank, vol. 37(4), pages 549-569.
    6. Dickinson, Jeffrey, 2020. "Planes, trains, and automobiles: what drives human-made light?," MPRA Paper 117126, University Library of Munich, Germany.
    7. Jeet Agnihotri & Subhankar Mishra, 2021. "Indian Economy and Nighttime Lights," Papers 2103.03179, arXiv.org.
    8. Kim, Kyoochul, 2022. "The North Korean economy seen by satellite: Estimates of national performance, regional gaps based on nighttime light," Journal of Asian Economics, Elsevier, vol. 78(C).
    9. Stein, Merlin, 2021. "Re-evaluating RCTs with nightlights - An example from biometric smartcards in India," University of Tübingen Working Papers in Business and Economics 152, University of Tuebingen, Faculty of Economics and Social Sciences, School of Business and Economics.
    10. Leonard Mushunje & Maxwell Mashasha, 2023. "Non-Banking Sector development effect on Economic Growth. A Nighttime light data approach," Papers 2401.08596, arXiv.org.
    11. Lai, Pingyao & Zhu, Tian, 2022. "Deflating China's nominal GDP: 2004–2018," China Economic Review, Elsevier, vol. 71(C).
    12. Dickinson, Jeffrey, 2020. "Planes, Trains, and Automobiles: Night-time Lights of the USA," MPRA Paper 100841, University Library of Munich, Germany.

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